1. Statement of the Technical Field
The invention concerns noise error amplitude reduction systems. More particularly, the invention concerns noise error amplitude reduction systems and methods for noise error amplitude reduction.
2. Description of the Related Art
In many communication systems, various noise cancellation techniques have been employed to reduce or eliminate unwanted sound from audio signals received at one or more microphones. Some conventional noise cancellation techniques generally use hardware and/or software for analyzing received audio waveforms for background aural or non-aural noise. The background non-aural noise typically degrades analog and digital voice. Non-aural noise can include, but is not limited to, diesel engines, sirens, helicopter noise, water spray and car noise. Subsequent to completion of the audio waveform analysis, a polarization reversed waveform is generated to cancel a background noise waveform from a received audio waveform. The polarization reversed waveform has an identical or directly proportional amplitude to the background noise waveform. The polarization reversed waveform is combined with the received audio signal thereby creating destructive interference. As a result of the destructive interference, an amplitude of the background noise waveform is reduced.
Despite the advantages of the conventional noise cancellation technique, it suffers from certain drawbacks. For example, the conventional noise cancellation technique does little to reduce the noise contamination in a severe or non-stationary acoustic noise environment.
Other conventional noise cancellation techniques generally use hardware and/or software for performing higher order statistic noise suppression. One such higher order statistic noise suppression method is disclosed by Steven F. Boll in “Suppression of Acoustic Noise in Speech Using Spectral Subtraction”, IEEE Transactions on Acoustics, Speech, and Signal Processing, VOL. ASSP-27, No. 2, April 1979. This spectral subtraction method comprises the systematic computation of the average spectra of a signal and a noise in some time interval and afterwards through the subtraction of both spectral representations. Spectral subtraction assumes (i) a signal is contaminated by a broadband additive noise, (ii) a considered noise is locally stationary or slowly varying in short intervals of time, (iii) the expected value of a noise estimate during an analysis is equal to the value of the noise estimate during a noise reduction process, and (iv) the phase of a noisy, pre-processed and noise reduced, post-processed signal remains the same.
Despite the advantages of the conventional higher order statistic noise suppression method, it suffers from certain drawbacks. For example, the conventional higher order statistic noise suppression method encounters difficulties when tracking a ramping noise source. The conventional higher order statistic noise suppression method also does little to reduce the noise contamination in a ramping, severe or non-stationary acoustic noise environment.
Other conventional noise cancellation techniques use a plurality of microphones to improve speech quality of an audio signal. For example, one such conventional multi-microphone noise cancellation technique is described in the following document B. Widrow, R. C. Goodlin, et al., Adaptive Noise Cancelling: Principles and Applications, Proceedings of the IEEE, vol. 63, pp. 1692-1716, December 1975. This conventional multi-microphone noise cancellation technique uses two (2) microphones to improve speech quality of an audio signal. A first one of the microphones receives a “primary” input containing a corrupted signal. A second one of the microphones receives a “reference” input containing noise correlated in some unknown way to the noise of the corrupted signal. The “reference” input is adaptively filtered and subtracted from the “primary” input to obtain a signal estimate.
Despite the advantages of the multi-microphone noise cancellation technique, it suffers from certain drawbacks. For example, analog voice is typically severely degraded by high levels of background non-aural noise. Although the conventional noise cancellation techniques reduce the amplitude of a background non-aural waveform contained in an audio signal input, the amount of the amplitude reduction is insufficient for certain applications, such as military applications, law enforcement applications and emergency response applications.
In view of the forgoing, there is a need in the art for a system and method to improve the intelligibility and quality of speech in the presence of high levels of background noise. There is also a need in the art for a system and method to improve the intelligibility and quality of speech in the presence of non-stationary background noise.